Conventional data converters provide either conversion from the analog domain to the digital domain in a typical analog-to-digital converter, or from the digital domain to an analog domain as a digital-to-analog converter. Typical analog-to-digital (A/D) converters of the delta sigma type provide some type of analog modulator for providing the initial data conversion, which is then followed by some type of filtering step. Conventionally, the filtering is performed in part in the digital domain. This requires some type of digital processing of the digital output of the modulator in the form of a digital filter such as a Finite Impulse Response (FIR) filter. However, the digital values output therefrom are values that exist in the time domain.
In some applications, it is desirable to determine information in the frequency domain after the conversion operation. Such applications as spectrum analyzers, for example, require such information. Therefore, the output of the data converter in the digital domain is then processed through some type of transform for converting time domain information to frequency domain information, this all completed in the digital domain.
The types of transform engines that are utilized to convert time domain information to digital domain information typically utilize some type of Fourier Transform, the most common being a Discrete Fourier Transform (DFT). The DFT is a one-to-one mapping of any finite sequence {y(r)}, r=0, 1, 2 . . . , N−1 of N samples onto another sequence. This is defined by the following relationship:       Y    ⁡          (      k      )        =            ∑              r        =        0                    N        -        1              ⁢                  y        ⁡                  (          r          )                    ⁢              w        N                  r          ⁢                                           ⁢          k                    where:             w      N        ≡          ⅇ                        -          j                ⁢                                   ⁢        2        ⁢                  π          /          N                      =            cos      ⁢                        2          ⁢          π                N              -          j      ⁢                           ⁢      sin      ⁢                        2          ⁢          π                N            
In general, a DFT algorithm requires a plurality of multiplication/accumulation operations. To reduce the number of these multiplication/accumulations, a Fast Fourier Transform (FFT) can be implemented to provide a rapid means for computing a DFT with Nlog2N multiplies, which otherwise would have taken N2 complex multiplications. Even with the reduction of the number of multiplications, there are still a large number of multiplication/accumulation operations that are required in order to calculate the time domain/frequency domain conversion. Conventionally, a Digital Signal Processor (DSP) is required which is typically a separate integrated circuit. As such, whenever providing for both a data conversion operation with an A/D converter, and a time domain/frequency domain conversion with a DSP, there are typically required two integrated circuits.
In general, there does not exist a commercial monolithic solution providing both the benefits of a data converter with that of a frequency domain converter such that an analog input can be received, converted to the digital domain and this digital value processed to provide a frequency domain output. In general, typical solutions utilize a data converter that provides a digital value in the time domain which is then input to a processor. This processor can be in the form of a microcontroller or a DSP. A data converter, due to its inherent construction, basically provides the ability to convert an analog input signal to a digital time domain output signal with a defined bit-resolution. This, of course, provides an output in the time domain. When processing this time domain signal to provide a frequency domain output, the processor is programmed to process some type of Discrete Fourier Transform or Fast Fourier Transform. Any type of algorithm that provides such a transform can be utilized. However, in order for a designer to utilize such a transform, this requires programming of the processor or microcontroller. Therefore, if an existing design must be upgraded to provide such a function or be required to process in the frequency domain, then a more complex DSP or microcontroller must be utilized. This is due to the fact that any processing in the frequency domain requires a more complex processing capability. The result is that an upgrade to a frequency domain solution from a time domain solution will probably require the designer to change his design to incorporate a much more complex processing section, in addition to also requiring a significant amount of programming of that processing section, this programming being the most expensive aspect of such an upgrade. It is desirable to utilize the pre-upgrade processing section, which is typically a relatively simple processor, and merely upgrade the data converter. However, the mere change of a design to process in the frequency domain as opposed to the time domain will necessitate additional processing capability and programming.
In addition to processing in the frequency domain, some applications require analysis of the statistics associated with a particular data conversion operation. These statistics can be utilized for the purpose of identifying when a particular parameter is fluctuating within its natural range, implying that no adjustment is desirable or necessary or when a parameter is behaving unnaturally, such that no maintenance or adjustment may be required. In modern embedded control systems, the data converters measure electrical parameters associated with the process, with several phenomena being monitored by a single data converter. Since these are “dumb” data converters, the digital output thereof is merely passed to a processor to perform some type of statistical operation thereon. At present, there is no monolithic solution that would allow statistical analysis to be performed prior to output of the actual digital value of the data conversion operation. This requires that all of the digital data be output, consuming significant I/O bandwidth.